Check out the candidates’ science and technology related policies here.

Of the three candidates, Senator Clinton has made the most visible attempt to publicize her proposals for science and technology policy. This was done in a speech last October, coinciding with the 50th anniversary of the Sputnik launch. Perhaps consistent with this appeal to history, her policies in S&T appear to be the most conventional of the three candidates. To be sure, this is not an area in which any of the candidates are being particularly innovative, and this is not an area that will swing a number of votes to a particular candidate. But if any of the candidates (or their campaigns) formed their S&T policies from the talking points of the various science and technology advocacy groups, Senator Clinton is the most likely choice.

If you look at the Senator’s Innovation Agenda you won’t see anything that hasn’t already been advanced by one advocacy group or another. In short, more research money, more people (through more fellowships and greater outreach to underrepresented groups), and more incentives for private sector investment (R&D tax credit changes, money for prizes, and various funds for alternative energy and green building). She wants to increase the budgets of both the NIH and (in a separate item/increase) the NSF, DOE Office of Science, and Department of Defense basic research budgets. Oddly enough, the amounts of increase for this group of agencies appears to be less than the doubling stipulated in the American Competitiveness Initiative (ACI). To be fair, the ACI covers NSF, NIST and the DOE Office of Science, but it would appear that Senator Clinton will favor the biomedical sciences over the physical sciences, when there is some consensus that the physical sciences are in greater need of assistance.

There are some parts of the Innovation Agenda that are different.

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Readers following recent threads know that I’ve been looking for instances where scientists make claims that some observations are “inconsistent with” the results from climate models. The reason for such a search is that it is all too easy for modelers to claim that anything and everything under the sun is “consistent with” their predictions, sometimes to avoid the perception of a loss of credibility in the political battle over climate change.

I am happy to report that claims of “inconsistent with” do exist. Here is an example from a paper just out by Knutson et al. in Nature Geoscience:

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[UPDATE] Roger Pielke, Sr. tells us that we are barking up the wrong tree looking at surface temperatures anyway. He says that the real action is in looking at oceanic heat content, for which predictions have far less variability over short terms than do surface temperatures. And he says that observations of accumulated heat content over the past 4 years “are not even close” to the model predictions. For the details, please see for your self at his site.]

“Skill” is a technical term in the forecast verification literature that means the ability to beat a naïve baseline when making forecasts. If your forecasting methodology can’t beat some simple heuristic, then it will likely be of little use.

What are examples of such naïve baselines? In weather forecasting historical climatology is often used. So if the average temperature in Boulder for May 20 is 75 degrees, and my prediction is for 85 degree, then any observed temperature below 80 degrees will mean that my forecast had no skill. In the mutual fund industry stock indexes are examples of naive baselines used to evaluate performance of fund managers. Of course, no forecasting method can always show skill in every forecast, so the appropriate metric is the degree of skill present in your forecasts. Like many other aspects of forecast verification, skill is a matter of degree, and is not black or white.

Skill is preferred to “consistency” if only because the addition of bad forecasts to a forecasting ensemble does not improve skill unless it improves forecast accuracy, which is not the case with certain measures of “consistency,” as we have seen. Skill also provides a clear metric of success for forecasts, once a naïve baseline is agreed upon. As time goes on, forecasts such as those issued by the IPCC should tend toward increasing skill, as the gap between a naive forecast and a prediction grows. If a forecasting methodology shows no skill then it would be appropriate to question the usefulness and/or accuracy of the forecasting methodology.

In this post I use the IPCC forecasts of 1990, 2001, and 2007 to illustrate the concept of skill, and to explain why it is a much better metric that “consistency” to evaluate forecasts of the IPCC.

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The IPCC will be using new scenarios for its future work, updating those produced in 2000, the so-called SRES scenarios. This would be good news, since, as we argued in Nature last month, the IPCC scenarios contain some dubious assumptions (PDF). But from the looks of it, it does not appear that much has changed, excpet the jargon. The figure below compares the new scenarios as presented in a report from a meeting of the IPCC held last month (source: PDF) with those from the 2000 IPCC SRES report. I have presented the two sets of scenarios on the same scale to facilitate comparison. Do they look much different to you?

In his latest essay on my stupidity, climate modeler James Annan made the helpful suggestion that I consult a “a numerate undergraduate to explain it to [me].” So I looked outside my office, where things are quiet out on the quad this time of year, but as luck would have it, I did find a young lady named Megan, who just happened to be majoring in mathematics who agreed to help me overcome my considerable ignorance.

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[Update: The ever helpful David Roberts of Grist Magazine points out that an op-ed in the Washington Times yesterday makes the same logical error that I point out in this post below made by Patrick Michaels -- namely that short-term predictive failures obviate the need for action. The op-ed quotes me and says that I am "not previously a global warming skeptic," which is correct, but implies that somehow I am now . . . sorry, wrong. It also quotes my conclusion that climate models are "useless" without the important qualifiers **for decision making in the short term when specific decisions must be made**. Such models are great exploratory scientific tools, and were helpful in bringing the issue of greenhouse gases to the attention of decision makers. I've emailed the author making these points, asking him to correct his piece.]

Here I’d like to explain why one group of people, which we might call politically active climate scientists and their allies, seek to shut down a useful discussion with intimidation, bluster, and name-calling. It is, as you might expect, a function of the destructive politics of science in the global warming debate.

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Over the next week I intend to make some posts about the science and technology policies of the three remaining major party candidates. With an eye toward generating discussion, I want to take a moment and note links to the candidates’ policies on science and technology. I am focusing on the candidates’ own statements or position papers from their websites. There are plenty of comparison websites, and they have their own perspective on the issues (and what ‘counts’ as science and technology issues).

This is intended as only a starting point. If I’m missing some resource that should be in the list below, please let us know in the comments.

Links after the jump, but two points worth noting. It’s rare to see all of a candidate’s positions related to science and technology all in one place. It’s even more rare to see them categorized as such. You’re more likely to see references to innovation and competitiveness or more issue specific areas (such as climate change and economic competitiveness).

Additionally, many campaign speeches and press releases are ill-described in search results or lists of media on these websites. I may very well have missed a relevant speech because the tagline was “Senator X Remarks at Iowa Jefferson-Jackson/Lincoln Day Dinner” and not “Senator X Remarks on Federal Research and Development Budgets”

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James Annan, a climate modeler, has written a post at his blog trying to explain why it is inconceivable that recent observations of global average temperature trends can be considered to be inconsistent with predictions from the models of the IPCC. James has an increasing snarky, angry tone to his comments which I will ignore in favor of the math (and I’d ask those offering comments on our blog to also be respectful, even if that respect is not returned), and in this post I will explain that even using his approach, there remains a quantitative justification for arguing that recent trends are inconsistent with IPCC projections.

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Lucia Liljegren has wonderfully clear post up which explains issues of consistency and inconsistency between models and observations using a simple analogy based on predicting the heights of Swedes.

She writes;

I think a simple example using heights is helps me explain the answer to these questions:

1. Is the mean trend in surface temperature over time predicted by the IPCC consistent with the temperature trends we have been experiencing? (That is: is 2C/century consistent with the trend we’ve seen? )
2. Is the lowest uncertainty bound the IPCC shows the public consistent with the trend in GMST (global mean surface temperature) we have seen since 2001?

I think these questions are important to the public and policy makers. They are the questions people at many climate blogs are asking and they are the questions many voters and likely policy makers would like answered.

I think the answer to both questions is “No, the IPCC predictions are inconsistent with recent data.”

Please go to her site and read the entire post.

She concludes her discussion as follows:

The IPCC projections remain falsified. Comparison to data suggest they are biased. The statistical tests accounts for the actual weather noise in data on earth.

The argument that this falsification is somehow inapplicable because the earth data falls inside the full range of possibilities for models is flawed. We know why the full range of climate models is huge: It contains a large amount of “climate model noise” due to models that are individually biased relative to the system of interest: the earth.

It will continue to admit what I have always admitted: When applying hypothesis tests to a confidence limit of 5%, one does expect to be wrong 5% of the time. It is entirely possible that the current falsification fall in the category of 5% incorrect falsifications. If this is so, the “falsified” diagnosis will reverse, and not we won’t see another one anytime soon.

However, for now, the IPCC projections remain falsified, and will do so until the temperatures pick up. Given the current statistical state ( a period when large “type 2″ error is expected) it is quite likely we will soon see “fail to falsify” even if the current falsification is a true one. But if the falsification is a “true” falsification, as is most likely, we will see “falsifications” resume. In that case, the falsification will ultimately stick.

For now, all we can do is watch the temperature trends of the real earth.

The literature on the myriad problems with the National Flood Insurance Program (NFIP) is long and deep. One of the main problems is that the program is not insulated from politics and thus is prevented from acting like a real market by setting actuarially-sound rates on its customers. Other problems exist but the premium-setting problem is the most significant and no matter how Congress tinkers with the NFIP, if it doesn’t address the premium issue then the NFIP will continue to be a taxpayer money-sucking problem child.

A NFIP reauthorization has been moving through Congress and yesterday the Senate passed its version. Predictably they moved the current $18+ billion NFIP deficit to general revenues (i.e. the U.S. taxpayer), a move that has a long history in Congress. But some good was included in the bill and the House’s version does not have the $18+ billion shift. The Senate was able to pass an annual premium increase cap from 10% to 15%, which is more significant than it probably sounds. They also authorized $2B for updated floodplain mapping, which is also much more significant than it probably sounds, as currently premiums are not always based on physical reality. (We’ll see how much actually gets appropriated out of that $2B.)

(And hey all, sorry for the non-controversial post.)